Day 1 2024

.gitignore

@@ -1,3 +1,4 @@
 .idea
+.vscode
 .ipynb_checkpoints
 __pycache__

solutions/2023/kws/aoc_2023_kws/day_09.py

@@ -0,0 +1,57 @@
+from functools import reduce
+
+import click
+from aoc_2023_kws.cli import main
+from aoc_2023_kws.config import config
+from aocd import submit
+
+
+def calculate_differences(value_series):
+    differences = []
+    for i in range(len(value_series) - 1):
+        differences.append(value_series[i + 1] - value_series[i])
+    return differences
+
+
+def find_order(value_series):
+    all_orders = [value_series]
+    while not all([f == 0 for f in value_series]):
+        value_series = calculate_differences(value_series)
+        all_orders.append(value_series)
+
+    return all_orders
+
+
+@main.command()
+@click.option("--sample", "-s", is_flag=True)
+def day09(sample):
+    if sample:
+        input_data = (config.SAMPLE_DIR / "day09.txt").read_text()
+    else:
+        input_data = (config.USER_DIR / "day09.txt").read_text()
+
+    input_data = input_data.splitlines()
+    value_series = [[int(value) for value in line.split()] for line in input_data]
+
+    all_predictions = []
+    for vs in value_series:
+        fit = find_order(vs)
+        last_numbers = [f[-1] for f in fit]
+        all_predictions.append(sum(last_numbers))
+        print(fit, sum(last_numbers))
+        print()
+
+    print(f"Part 1: {sum(all_predictions)}")
+
+    all_predictions = []
+    for vs in value_series:
+        fit = find_order(vs)
+        first_numbers = [f[0] for f in fit]
+        first_numbers.reverse()
+        prediction = reduce(lambda x, y: y - x, first_numbers)
+        all_predictions.append(prediction)
+
+        print(fit, prediction)
+        print()
+
+    print(f"Part 2: {sum(all_predictions)}")

solutions/2023/kws/aoc_2023_kws/day_10.py

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+import math
+from functools import cached_property
+from typing import Dict, List, Tuple
+
+import click
+import numpy as np
+from aoc_2023_kws.cli import main
+from aoc_2023_kws.config import config
+from aocd import submit
+
+SEGMENTS = (
+    ("|", {"N", "S"}),
+    ("-", {"E", "W"}),
+    ("L", {"N", "E"}),
+    ("J", {"N", "W"}),
+    ("7", {"S", "W"}),
+    ("F", {"S", "E"}),
+)
+connections = {k: v for k, v in SEGMENTS}
+
+
+class Pipe:
+    def __init__(self, x, y, char):
+        self.pos = x, y
+        self.char = char
+        self.connections = set()
+        self.level = None
+        if char in connections:
+            self.connections = connections[char]
+
+    @cached_property
+    def connected_tiles(self) -> Tuple[int, int]:
+        x, y = self.pos
+        tiles = []
+        if "N" in self.connections:
+            tiles.append((x, y - 1))
+        if "S" in self.connections:
+            tiles.append((x, y + 1))
+        if "W" in self.connections:
+            tiles.append((x - 1, y))
+        if "E" in self.connections:
+            tiles.append((x + 1, y))
+        return tuple(tiles)
+
+    def __repr__(self) -> str:
+        return f"Pipe({self.char}{self.pos}, {self.connections})"
+
+
+@main.command()
+@click.option("--sample", "-s", is_flag=True)
+def day10(sample):
+    if sample:
+        input_data = (config.SAMPLE_DIR / "day10.txt").read_text()
+    else:
+        input_data = (config.USER_DIR / "day10.txt").read_text()
+
+    input_data = input_data.splitlines()
+    input_data = [list(line) for line in input_data]
+
+    pipes = []
+    for y, row in enumerate(input_data):
+        for x, char in enumerate(row):
+            if char == ".":
+                continue
+            else:
+                pipe = Pipe(x, y, char)
+                pipes.append(pipe)
+
+    all_connections: Dict[tuple, List[Pipe]] = {}
+    all_pipes = {p.pos: p for p in pipes}
+    start = None
+
+    for pipe in pipes:
+        for c in pipe.connected_tiles:
+            all_connections.setdefault(c, []).append(pipe)
+
+        if pipe.char == "S":
+            start = pipe
+
+    # Get all the pipes that are connected to any of the pipes in loop_pipes
+    def find_connections(loop_pipes, level):
+        new_pipes = []
+        for p in loop_pipes:
+            new_pipes.extend([all_pipes.get(c) for c in p.connected_tiles])
+        new_pipes = [p for p in new_pipes if p]
+        new_pipes = [p for p in new_pipes if p.level is None]
+        for p in new_pipes:
+            p.level = level
+        return new_pipes
+
+    loop_pipes = all_connections[start.pos]
+    for loop_pipe in loop_pipes:
+        loop_pipe.level = 1
+
+    new_pipes = loop_pipes
+    loop_pipes = [start] + loop_pipes
+    level = 2
+    while new_pipes:
+        new_pipes = find_connections(new_pipes, level)
+        loop_pipes.extend(new_pipes)
+        level += 1
+
+    print(max([p.level for p in loop_pipes]))
+
+    # F-7 = 2
+    # F-J = 1
+
+    # Part 2
+    matrix = np.array(input_data)
+    loop_coords = [p.pos for p in loop_pipes]
+    for y in range(matrix.shape[0]):
+        for x in range(matrix.shape[1]):
+            if (x, y) not in loop_coords:
+                matrix[y, x] = "."
+
+    for y in matrix:
+        print("".join(y))
+
+    print("\n\n")
+
+    inside_pieces = 0
+    replacement_values = {"F": -0.5, "J": -0.5, "7": 0.5, "L": 0.5, "|": 1}
+    for y in range(matrix.shape[0]):
+        for x in range(matrix.shape[1]):
+            if matrix[y, x] not in ".*":
+                continue
+
+            x_after = matrix[y, x:]
+            x_values = [replacement_values.get(c, 0) for c in x_after]
+            x_after_odd = math.floor(abs(sum(x_values))) % 2
+
+            if x_after_odd:
+                matrix[y, x] = " "
+                inside_pieces += 1
+
+    for y in matrix:
+        print("".join(y))
+
+    print(inside_pieces)

solutions/2023/kws/aoc_2023_kws/day_11.py

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+import click
+import numpy as np
+from aoc_2023_kws.cli import main
+from aoc_2023_kws.config import config
+from aocd import submit
+from scipy.spatial.distance import pdist
+
+
+@main.command()
+@click.option("--sample", "-s", is_flag=True)
+def day11(sample):
+    if sample:
+        input_data = (config.SAMPLE_DIR / "day11.txt").read_text()
+    else:
+        input_data = (config.USER_DIR / "day11.txt").read_text()
+
+    input_data = input_data.splitlines()
+    input_data = [list(line) for line in input_data]
+    orig_data = np.array(input_data)
+    input_data = orig_data.copy()
+
+    # Find the rows that only contain "." entries
+    rows_with_only_dots = np.all(input_data == ".", axis=1)
+    indices_of_rows_with_only_dots = np.where(rows_with_only_dots)[0]
+
+    # Find the columns that only contain "." entries
+    cols_with_only_dots = np.all(input_data == ".", axis=0)
+    indices_of_cols_with_only_dots = np.where(cols_with_only_dots)[0]
+
+    # Insert a column of "."s before each row
+    input_data = np.insert(input_data, indices_of_rows_with_only_dots, "x", axis=0)
+
+    # Insert a column of "."s before each column
+    input_data = np.insert(input_data, indices_of_cols_with_only_dots, "y", axis=1)
+
+    # Find the x, y coordinates of each '#'
+    coordinates = np.argwhere(input_data == "#")
+
+    # Calculate pairwise distances between coordinates
+    pairwise_distances = pdist(coordinates, metric="cityblock")
+
+    # Print the pairwise distances
+    print(pairwise_distances)
+    print(len(pairwise_distances))
+    print(sum(pairwise_distances))
+
+    test = [[0, 4], [11, 9]]
+    print(pdist(test, metric="cityblock"))
+
+    part2_data = orig_data.copy()
+    coordinates = np.argwhere(part2_data == "#")
+
+    scale = 1_000_000
+    # scale = 100
+
+    scale -= 1
+
+    print("Adjusted coordinates")
+    adjusted_coordinates = []
+    for x, y in coordinates:
+        cols = sum([1 for i in indices_of_cols_with_only_dots if i < y]) * scale
+        rows = sum([1 for i in indices_of_rows_with_only_dots if i < x]) * scale
+        if cols:
+            y += cols
+        if rows:
+            x += rows
+        print(x, y, cols, rows)
+        adjusted_coordinates.append([x, y])
+
+    pairwise_distances = pdist(adjusted_coordinates, metric="cityblock")
+    print(sum(pairwise_distances))